Application of neural networks for short-term load forecasting

This paper presents the development of an artificial neural network based short-term load forecasting model. The model can forecast daily load profiles with a load time of one day for next 24 hours. In this method can divide days of year with using average temperature. Groups make according linearity rate of curve. Ultimate forecast for each group obtain with considering weekday and weekend. 24 hours of a day divided to 3 groups at 8 hours, network for every each of eight varieties must interpolate. This paper investigates effects of temperature and humidity on consuming curve. For forecasting load curve of holidays at first calculate pick and valley and then the neural network forecast is re-shaped with the new data. The networks are trained using hourly historical load data and daily historical max/min temperature and humidity data. The results of testing the system on data from yazd utility are reported.